Magnetic Flux Leakage Technique Research Articles

Uplifting the study of the inline inspection technique on the buckling pipelines in pipeline integrity management strategy

This work reports the development of inline inspection (ILI) methodology to enhance the pigging activity for the dented pipeline, facilitate the pigging process to prevent Pipeline Inspection Gauge (PIG) from getting stuck and improve the safety passage for buckled pipelines. The recent report unveils the condition of the UNPIGGABLE pipelines, which reduce the inner diameter of pipelines to 257.51 mm, equivalent to 27.58 % of the initial diameter and restricts the pigging activity. In this report, the pull-through test coupled with the collapsibility test was conducted. The success of the test above allows the ILI equipment based on the magnetic flux leakage (MFL) technique to record the internal and external wall loss inwardly and geometric defect on diameter of the pipelines. The prepared artificial dented pipeline was made before it underwent several tests. Based on the pull-through test, the maximum force of 27000 N is more significant than the pipeline operating pressure to enable the MFL tool to pass through the pipelines despite exhibiting the geometry anomaly. Compressing the opposite magnetic yoke of the collapsibility test is critical, showing that the ILI MFL tool reaches its maximum compression of 242 mm. The value is lower than the minimum internal diameter of 257 mm. The ILI results show that the highest metal loss was achieved at 73 % at 15504 m at the bottom of the inspected pipelines. At the same time, the dented area reduces to more than 6 % of the pipelines’ nominal outer diameter and imposes the pipe’s integrity status to red. The distinctive result of the research can be used to model the future unprecedented pigging process when buckles appear in pipelines

Development of Non-destructive Testing Method for Tube Inspection in Fin-Fan Coolers in Kazakhstan’s Oil/Gas, Chemical and Power Industries.

Many oil/gas fields in Kazakhstan contain high levels of highly corrosive H2S and CO2, sometimes at very high pressures. The management of corrosion is essential in maintaining plant safety and integrity of the processing facility. This paper describes the development of a non-destructive testing (NDT) method that improves the reliability of air-cooled heat exchangers by reducing down-time related to corrosive and erosive failure of fin-fan tubes. The project goal was to maximize the output of oil and gas plants and refineries while reducing the plant operating cost. The work first identified those NDT requirements for air-cooled heat exchangers damage assessment that would provide the greatest economic benefit for Kazakhstan industry. The main objective was to develop a state-of-an-art NDT method for air-cooled heat exchanger tubes, capable to: a) detect any damage mechanism while testing from tube internal diameter, b) accurately determine the damage in terms of wall loss, c) perform inspection quickly and expediently, d) requires minimum tube cleaning. Consequently, the method specially adapted for Kazakhstan conditions was developed based on a combination of Magnetic Flux Leakage (MFL) technique for flaw detection and with Hall effect measurement of wall thickness and gradual corrosion in tubes. The method has been tested in both laboratory and field conditions and the results were compared with accurate but slow ultrasonic IRIS method. High correlation was obtained, which proved that the developed technology is capable to deliver similar results at the speed almost 10 times faster and less than half the cost.

Detection of crack on a mild steel plate by using a magnetic probe incorporating an array of fluxgate sensors

Magnetic flux leakage (MFL) and eddy current testing (ECT) are two of the most commonly used techniques to detect cracks in ferromagnetic materials. The MFL technique excels in determining the locality of a crack, while the ECT technique can provide useful information regarding the dimension and orientation of cracks. The objective of this research is to propose a simultaneous application of both MFL and ECT techniques in a single probe and to improve the visual resolution of the defect by superimposing signals from a sensor array. A magnetic probe was developed with an array of fluxgate sensors and two excitation coils. Line scan measurements and 2D mapping of artificial slits with different depths were conducted on a mild steel SS400 plate with a thickness of 12 mm to evaluate the performance of the developed probe. The line scan measurement results demonstrated the ability of the probe to detect the presence of artificial slits on the sample and to predict the depth of the slits, especially at the 110-Hz excitation frequency. Then, from the 2D mapping results, the location of the slit was determined. Furthermore, by obtaining a superimposed 2D map generated by each sensor, an improved 2D map resolution with lower background noise was obtained.

A stress defect state measurement method based on low-frequency ACMFL excitation and Hall sensor array collection

The safety testing of ferromagnetic materials, which are the main materials for various machines and equipment, is particularly important. Stress concentration zones (stress defects) cause stress corrosion of ferromagnetic materials, and also have the potential to cause direct damage to ferromagnetic materials. Estimation of stress sources state using electromagnetic nondestructive measurement methods is a critical and difficult problem. In this paper, a visual and intelligent identification method of stress defects in ferromagnetic materials by low frequency AC magnetic flux leakage (ACMFL) technique is proposed. A new three-point compression experiment was designed in this paper. Time-difference vision is established to analyze the ACMFL signal caused by stress defects. A visual transformed convolutional neural network deep learning algorithm has been proposed to identify grayscale patterns pre-processed by the time-difference vision. The results show that the method proposed in this paper elucidates the relationship between the time-difference vision of a stress defect and the stress source state of the mechanical stress. Our proposed method allows to analyze the pressure indenter size of the pressure source of stress defects.

Effect of flaw orientation on magnetic flux leakage and remote field eddy current inspection of small diameter steel tubes

ABSTRACT This paper presents effect of flaw orientation on magnetic flux leakage (MFL) and remote field eddy current (RFEC) inspection of small diameter (19.05-mm outer diameter, OD and 2.77-mm wall thickness, WT) seamless low carbon steel tubes. Artificial electro-discharge machined (EDM) notches in three carbon steel Tubes A (orientations from 0° to 90° in steps of 10° and 50% WT depth), B (70° orientation and depths from 10% WT to 100% WT in steps of 10% WT) and C (50° orientation and depths from 10% WT to 60% WT in steps of 10% WT) were used to investigate the detectability of notches with different orientations and depths. Studies on different notch orientations reveal that RFEC technique detected all the notches with good signal-to-noise ratio (SNR), while MFL technique detected the notches with orientations from 40° to 90°. The effect of notch orientation is observed more in MFL technique as compared to RFEC technique. Further, the effects of scanning speed and notch depth for the detection of oriented notches were studied. Studies reveal that the effect of scanning speed is also more (40 times) dominant in MFL technique as compared to RFEC technique. Studies also reveal that the scanning effect is again dependent upon the notch depth. For 70° oriented notches, MFL technique is found to be more sensitive to notch depth as compared to RFEC technique. For 50° oriented notches, RFEC technique is found to be more sensitive to notch depth as compared to MFL technique.

Real-Time Inspection in Detection Magnetic Flux Leakage by Deep Learning Integrated with Concentrating Non-Destructive Principle and Electromagnetic Induction

One of the most common techniques of pipeline inspection is magnetic flux leakage (MFL). It is a non-destructive testing (NDT) method that employs magnetic sensitive sensors to detect MFL of faults on pipelines' internal and external surfaces. This research proposed a novel technique in real-time detection of MFL with pattern recognition in non-destructive principle using deep learning architectures. Here, the MFL signal has been collected as a large data sequence which has to be trained and validated using neural networks. Initially, the MFL has been detected using Faraday's law of electromagnetic induction (EMI) which is induced with Z-filter in electromagnetic (EM) decomposition. The collected signal of MFL has been classified using convolutional neural network (CNN), and this classified signal has been recognized by the patterns based on their threshold of the signal. By extracting and analyzing magnetic properties of MFL for a signal, the quantitative MFL has exceeded their threshold value from detected signals. Damage indices based on the link between enveloped MFL signal and the threshold value, as well as a generic damage index for MFL technique, were used to strengthen the quantitative analysis.

Analysis of Leakage in a Sustainable Water Pipeline Based on a Magnetic Flux Leakage Technique

Pipelines are typically used to transport oil, natural gas, water, etc. It is one of the most effective methods for transferring fluids over long distances. However, long-term usage of these pipes without maintenance results in the formation of residues, which will pave the way for pipeline accidents and soil contamination. To ensure the safety and protection of resources, these sustainable pipelines need to be inspected to avoid losses. This work aims to investigate various internal defect leaks in the non-uniform thickness of sustainable water pipes that are joined with a pipe expander. The magnetic flux leakage technique was implemented to evaluate these defects by means of a flexible GMR sensor array. An inspection robot containing two units was fabricated with the aid of 3D printing. The power unit provides the necessary thrust to actuate the entire robot whereas the sensing unit is responsible for analyzing the leaks. The robot’s movement is predicted by the MPU6050 and ultrasonic distance sensors that are plotted as motion plots. The sensing unit consists of permanent magnets and a giant magnetoresistance (GMR) array to interrogate the flux leakage in the defect region. The flux leakage from the defects was stored with the help of an Arduino microcontroller, which controls the overall process. In addition, the spring suspension is provided to regulate the motion of the robot. The flux leakage from the defect region was plotted as waveform graphs. Thus, the results are effectively presented and compared. The calculated signal-to-noise ratio (SNR) of the magnetic flux leakages (MFLs) for 4.5 mm-thick pipe defects was 12 to 20.8 dB, and for 6.52 mm-thick pipe defects, it was 9.5 to 19 dB. In sum, the MFL technique provides a reliable method for the sustainable development of water supply to wide areas.

Surface and back-side defects identification combined with magnetic flux leakage and boundary magnetic perturbation.

In magnetic flux leakage (MFL) detection, the identification of surface and back-side defects is required to obtain more accurate defect quantification and risk assessment results. However, current MFL techniques can detect both surface and back-side defects but are generally unable to distinguish between them. Therefore, this paper proposes a new boundary magnetic perturbation (BMP) testing method, combining the results of MFL to distinguish between surface and back-side defects. First, the detection mechanism of the BMP testing method and the impact of the tested magnetic flux density components are presented and analyzed by simulations to further develop an identification method. Then, the influences of the BMP sensor's lift-off and installation position are investigated by experiments to improve distinguishing performance. Finally, the repeated measurements show that the surface and back-side defects within the wide range of sizes can be identified accurately, even when the defect depths are in the range of 12.5%-87.5% of the sample thickness. Furthermore, the BMP testing method neither increases the length of the detection device nor requires additional magnetizers or signal generators. Therefore, the proposed method is highly suitable for the existing MFL detection devices to distinguish between surface and back-side defects.

A novel identification approach for corrosion and gouging of oil and gas pipelines based on low magnetisation level MFL inspection

The ability to characterise corrosion and gouging associated with metal loss and the identification of gouge type from metal loss defect types are two of the primary obstacles affecting magnetic flux leakage (MFL) internal inspection technology. Gouges in pipelines are not extraordinarily severe; however, the depth of corrosion increasing due to a certain corrosion rate can be quite serious. In this paper, a novel theoretical model combined with a new approach for the analysis of the signals that distinguish gouging and corrosion using a low magnetisation level is presented for MFL detection, since the traditional MFL internal detection tools are insensitive to stress characteristics in the case of saturation magnetisation. A two-stage finite element (FE) model for the prediction of magnetic flux leakage resulting from two types of defect is built. In the first stage, the stress distribution associated with gouging is obtained from a solid mechanics model and, in the second stage, the stress distribution is incorporated into a magnetic finite element model by mapping the stress levels to permeability. The possibility of detection and identification of corrosion and gouging using the MFL technique at low-level magnetisation was confirmed by experimentally comparing the characteristics of the MFL signals for each defect type.

In-line detection of defects in steel pipes using flexible GMR sensor array

Steel pipes serve as the main source for transporting water, gas, and other petrochemical substances for longer distances. These pipes were able to withstand extreme weather conditions and hostile environments because of their remarkable properties such as higher strength, durability, lower cost, and improved wear and corrosion resistance. However, prolonged usage of these pipes in such environments may lead to the initiation of defects in their inner surface such as leak holes, cracks, corrosion, etc. In overtime, these defects may become more severe, resulting in component failure and property losses. Hence, earlier detection of defects is highly recommended to avoid these failures. In this work, an in-line robot system has been proposed for detecting the defects in the steel pipes. This robot utilizes a non-destructive way for evaluating the flaws by means of the magnetic flux leakage (MFL) technique. A 3D finite element model has been developed with the aid of ANSYS Maxwell 3D software for evaluating the generated magnetic field and optimizing the lift-off distance. The permanent magnet is preferred as the magnetizing material for implementing local magnetization in the inspection area. The magnetic flux leakage from the defect region is sensed by using a flexible GMR sensor array of six sensors. Artificial defects were introduced in a 6-inch diameter steel pipe in various shapes and the Arduino UNO controls the overall process. The data from the sensor array were collected using the Arduino and plotted as the waveform graph. From this graph, the voltage variations among the sensors represent the defect region. In addition, the higher peak in amplitude denotes that the flux is influenced by the defect’s depth. Thus, the waveform graph for the introduced defects was analyzed and all graph represents a better signal to noise ratio (SNR) for identifying the defects.

Fast, Robust, and Low-Cost Microwave Imaging of Multiple Non-Metallic Pipes

The use of non-metallic pipes and composite components that are low-cost, durable, light-weight, and resilient to corrosion is growing rapidly in various industrial sectors such as oil and gas industries in the form of non-metallic composite pipes. While these components are still prone to damages, traditional non-destructive testing (NDT) techniques such as eddy current technique and magnetic flux leakage technique cannot be utilized for inspection of these components. Microwave imaging can fill this gap as a favorable technique to perform inspection of non-metallic pipes. Holographic microwave imaging techniques are fast and robust and have been successfully employed in applications such as airport security screening and underground imaging. Here, we extend the use of holographic microwave imaging to inspection of multiple concentric pipes. To increase the speed of data acquisition, we utilize antenna arrays along the azimuthal direction in a cylindrical setup. A parametric study and demonstration of the performance of the proposed imaging system will be provided.

Development of an MFL Coil Sensor for Testing Pipes in Extreme Temperature Conditions.

This paper aims to design a coil sensor for corrosion monitoring of industrial pipes that could detect variations in thickness using the MFL (Magnetic Flux Leakage) technique. An MFL coil sensor is designed and tested with pipe sample thicknesses of 2, 4, 6, and 8 mm based on the magnetic field effect of ferrite cores. Moreover, a measurement setup for analysing pipe samples up to a temperature of 200° Celsius is suggested. Experimental results reveal that the MFL coil sensor can fulfil the requirements for MFL testing of pipes in high temperature conditions, and that the precision of MFL monitoring of pipes to detect corrosion at high temperatures can be improved significantly.

Pipeline corrosion defect parameterisation with magnetic flux leakage inspection: a contextual representation approach

Corrosion is one of the significant reasons for oil and gas pipeline failures. In pipeline integrity management programmes, the magnetic flux leakage (MFL) technique is widely used to detect and quantify corrosion defects. The inspection results of MFL list the profiles of individual corrosion defects; however, the structural safety of a pipeline not only depends on the size of individual corrosion defects but also the pattern of closely spaced defects. To achieve a contextual defect representation, the concept of parameterisation, which considers the adjacent defects as additional information of the central defect, is proposed in this study. The process through which to realise this contextual representation is described in this paper. Three parameterisation models are proposed and a two-dimensional Gaussian function is employed to model the interaction strength between adjacent defects. The experimental results demonstrate that the shape context (SC) model associated with the interaction strength function (ISF) shares the highest similarity with human inspectors in comparison with the other two models. Thus, the proposed parameterisation approach can be used to retrieve similar corrosion defects and analyse defect population distribution along a pipeline.

Extraction of Flux Leakage and Eddy Current Signals Induced by Submillimeter Backside Slits on Carbon Steel Plate Using a Low-Field AMR Differential Magnetic Probe

Magnetic Flux Leakage (MFL) and Eddy Current Testing (ECT) are commonly employed as the non-destructive evaluation (NDE) techniques used to detect defects within the steel. The MFL technique is advantageous in terms of deep defects detection, while the ECT technique excels in providing dense information regarding defects. In this work, artificial MFL and eddy current (EC) signals in ferromagnetic materials are studied, and an experimental magnetic probe that utilizes both techniques is developed for signal verification. The separation between MFL and EC signals is achieved by utilizing the phase-sensitive detection technique, implementing a dynamic referencing method as opposed to the conventional static phase referencing. A finite element method (FEM) based simulation is employed to study and verify the MFL and EC signals measured by the proposed magnetic probe. The proposed magnetic probe features highly sensitive anisotropic magneto-resistive sensors capable of measuring the MFL and EC signals induced by artificial slits of varying depths engraved onto a 2-mm carbon steel plate. Finite element simulations indicate different flux leakage patterns and eddy current signals detected in the vicinity of the back-side slits. A good agreement is observed between the simulated and the measured MFL and EC signals for the optimized frequency range of 110-210 Hz with the corresponding Lissajous curve for the detection of submillimeter back-side slits. The study has shown that the combination of MFL and EC signals can be successfully captured by an appropriate magnetic probe for an enhanced detection performance of back-side defects in ferromagnetic materials.

Application of Artificial Neural Network and Multi-magnetic NDE Methods to Determine Mechanical Properties of Plain Carbon Steels Subjected to Tempering Treatment

The present paper shows the results of applying an artificial neural network to three non-destructive magnetic methods including magnetic hysteresis loop (MHL), eddy current (EC), and magnetic flux leakage (MFL) techniques to determine mechanical features of plain carbon steels with unknown carbon contents subjected to tempering treatment. To simultaneously evaluate the effects of carbon content and microstructure on the magnetic and mechanical properties, four grades of hypoeutectoid steel samples containing 0.30, 0.46, 0.54, and 0.71 wt.% carbon were austenitized in the range of 830-925 °C and then subjected to quench-tempering treatments at 200, 300, 400, 500 and 600 °C. In the next step, mechanical properties including tensile strength, elongation, and hardness were measured using tensile and hardness tests, respectively. Finally, to study the electromagnetic parameters, MHL, MFL and EC non-destructive electromagnetic tests were applied to the heat-treated samples and their outputs were fed to a generalized neural network designed in this work. The results revealed that using a proper combination of electromagnetic parameters as the ANN input for each mechanical parameter enables us to determine the hardness, UTS and elongation of hypoeutectic carbon steel parts after tempering treatment with high accuracy.

Detection of localised flaws in small-diameter carbon steel tubes using multi-NDE techniques

This paper proposes multi-NDE techniques for enhanced and reliable detection of localised defects or flaws in small-diameter (19.05 mm outer diameter (OD) and 2.77 mm wall thickness (WT)) low-carbon steel tubes. Multi-NDE techniques comprising magnetic flux leakage (MFL), remote field eddy current (RFEC) and the ultrasonic internal rotary inspection system (IRIS) are used to investigate the detectability and spatial resolution for the detection of localised flaws of different types and sizes, which simulate flaws generally formed in carbon steel tubes. The studies reveal that the MFL and IRIS techniques detect 0.55 mm-deep holes and notches while the RFEC technique detects 1.11 mm-deep holes and 0.55 mm-deep notches. The IRIS technique is capable of resolving axial and circumferential holes (1 mm diameter and 1.39 mm depth) of 2 mm hole-to-hole separation, while the MFL technique can resolve axial holes of 2 mm hole-to-hole separation but not circumferential holes. The RFEC technique can detect axial holes as a single hole but is unable to detect circumferential holes, resulting in no spatial resolution for the axial and circumferential holes. Compared to the MFL and RFEC signals, IRIS readily discerns the spatial extent of the flaws with the possibility of sizing from the images. The study also reveals that the resolution capability of the IRIS technique is influenced by the speed of the turbine rotation inside the tube.

Analysis of Magnetic-Flux Leakage (MFL) Data for Pipeline Corrosion Assessment

Oil and gas pipelines transport and distribute large quantities of oil products and natural gas to industrial and residential customers over a long distance. However, pipeline failures could lead to enormous hazards and safety issues. Metal loss, due to corrosion, is a significant cause for pipeline failures. To detect and quantify metal loss, in-line inspection (ILI) is carried out periodically to assess the integrity of the pipeline. Among all the ILI techniques, magnetic-flux leakage (MFL) is the most popular one due to its high efficiency, robustness, and good applicability to both oil and gas pipelines. This article provides a comprehensive review of the pipeline corrosion assessment with the MFL technique from the data analytic perspective. The analyses of the MFL signal and data contribute to both corrosion quantification and prediction. In this article, the state of the art for the MFL measurement technique is briefly reviewed first. For corrosion quantification, the signal processing methods together with the characterization models, which aim to enhance the measurement and characterize the corrosion, are described and discussed. For corrosion prediction, this article investigates multiple MFL data matching methods, which align defects from successive ILI runs. Subsequently, corrosion growth models, which aim to predict the future corrosion status, are presented. Besides, the reliability analysis of the corroded pipeline is reviewed. The potential of fusing MFL with other non-destructive testing (NDT) techniques are explored as well. At the end of this article, we summarize the existing issues and describe the trends for future research on pipeline corrosion assessment.

Practical evaluation of velocity effects on the magnetic flux leakage technique for storage tank inspection

Magnetic flux leakage (MFL) is a technique commonly used to inspect storage tank floors. This paper describes a practical evaluation of the effect of scanning velocity on defect detection in mild steel plates with thicknesses of 6 mm, 12 mm and 16 mm using a fixed permanent magnetic yoke. Each plate includes four semi-spherical defects ranging from 20% to 80% through-wall thickness. It was found that scanning velocity has a direct effect on defect characterisation due to the distorted magnetic field resulting from induced eddy currents that affect the MFL signal amplitude. This occurs when the inspection velocity is increased and a reduction in the MFL signal amplitudes is observed for far-surface defects. The opposite applies for the top surface, where an increase is seen for near-surface MFL amplitudes when there is insufficient flux saturating the inspection material due to the concentration of induced flux near the top surface. These findings suggest that procedures should be altered to minimise these effects based on inspection requirements. For thicker plates and when far-surface defects are of interest, inspection speeds should be reduced. If only near-surface defects are being considered then increased speeds can be used, provided that the sensor range is sufficient to cope with the increased signal amplitudes so that signal clipping does not become an issue.

Magnetic flux leakage technique to detect loss in metallic area in external post-tensioning systems

Post-tensioned girders with internal and external tendons are widely used for the construction of bridges because of their eminent advantages. However, owing to poor grout quality, damage to protective ducts, exposure to environmental conditions, overstressing of strands, and overloading of the girder, the external tendons are susceptible to corrosion and breakage of strands. It is important to identify loss in cross-sectional area of the strands, in order to take preventive actions to avoid loss in capacity, and in some cases even failure of the tendon. Magnetic Flux Leakage (MFL), a non-destructive evaluation technique, is used to detect and quantify metallic defects in external post-tensioning systems. In order to evaluate this technique in in-service bridges under field conditions, a full-scale post-tensioned girder specimen was constructed as a control specimen. Metal defects that include section loss, corrosion, and breakage in wire and single or multiple strands with known loss in cross-sectional area, were fabricated and placed in the external tendons. The MFL device is then used to evaluate loss in metallic area (LMA) in the control girder specimen. The MFL device consistently detects LMA greater than 5% of the total strand cross-sectional area for the group of strands in a tendon. The measurements are found to be repeatable and reproducible. Even though there are errors associated with the estimated LMA percentages, especially for defects with greater than 50% loss in cross-sectional area, the estimated magnitude of the loss in total strand cross-sectional area gives a good indication about the severity of the defect.

Identification and Sizing of Neighboring Pits Using Three-Axis Magnetic Flux Leakage Technique

Neighboring corrosion pits are one of the most frequently found defects in gas pipelines. Because of the interaction with each other, these defects accelerate the corrosion growth rates in the wall of the pipelines. Hence, it is meaningful to discriminate multiple neighboring pits from single isolated pits by analyzing their characteristics. This article presents the results of a study on the use of a three-axis magnetic flux leakage (MFL) technique for the identification and sizing of neighboring corrosion pits. Various patterns of two neighboring pits are considered by modifying the separation distances between them. Finite element modeling as well as experimental measurements is used to investigate the spatial distributions of the three MFL components (axial, radial, and circumferential) generated by the neighboring pits. The results indicate that the axial as well as the radial MFL components of the neighboring pits merge to one signature the closer the two defects are, and thus, no width sizing of the individual pits is possible anymore. In contrast, both pits are still discernible in the circumferential MFL signatures. The described method can help to identify neighboring corrosion pits and thus improve the sizing accuracy of MFL measurements.